The occurrence and intensity of rainfall can have a significant impact for agricultural growers. Data relating to precipitation can be extremely valuable to agricultural growers in making strategic decisions. Computer-based agronomic models may rely on historical observations of precipitation to create forecasts for future precipitation.
While precipitation data is extremely valuable, it is not always readily available. While many areas are currently monitored by satellite or radar, some areas only receive current precipitation data through rain gauges at specific locations. Other areas are currently monitored by satellite or radar, but historically only received precipitation data through rain gauges.
Because of the lack of both historical data and current data in some locations, it becomes important to create reliable estimates for rainfall at precise locations. Current modeling techniques can be used to estimate rainfall at given points based on gauge observations. Various problems exist with modern modeling techniques. First, many modeling techniques fail to take into account the spatial correlation of rainfall such that the probability of rainfall at a given location will sharply increase if neighboring gauges observed rainfall. Second, while current models can estimate rainfall at given locations, creating large scale models based on a wide array of observations becomes computationally prohibitive. Finally, while many modeling techniques can estimate rainfall at a given location, they are unable to propagate probabilistic estimates. In creating a large scale field, creating accurate estimates becomes even more difficult as the point estimates do not include the propagating error that would exist in a probabilistic estimate.